DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity

1 February 2022

Papers citing "DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity"

6 / 6 papers shown

Title
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme Qianyu Long Qiyuan Wang Christos Anagnostopoulos Daning Bi FedML 28 0 0 24 Apr 2024
Compressed and Sparse Models for Non-Convex Decentralized Learning Andrew Campbell Hang Liu Leah Woldemariam Anna Scaglione 28 0 0 09 Nov 2023
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 187 412 0 14 Jul 2021
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning Xin Zhang Jia Liu Zhengyuan Zhu Elizabeth S. Bentley 49 14 0 04 May 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 176 764 0 28 Sep 2019
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 139 1,205 0 16 Aug 2016